Analysis of social information for author recommendation

被引:0
|
作者
Heck, Tamara [1 ]
机构
[1] Heinrich Heine Univ Dusseldorf, Abt Informat Wissensch, Dusseldorf, Germany
来源
INFORMATION-WISSENSCHAFT UND PRAXIS | 2012年 / 63卷 / 04期
关键词
researcher; recommendation system; citation index; bibliographic coupling; author-co-citation; empirical study; Web of Science; Scopus; CiteULike;
D O I
10.1515/iwp-2012-0048
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Researchers in almost all scientific disciplines rely heavily on the collaboration of their colleagues. Throughout his or her career, any researcher will build up a social academic network consisting of people with similar scientific interests. A recommendation system could facilitate the process of identifying and finding the right colleagues, as well as pointing out possible new collaborators. As a researcher's reputation is of great importance, the social information gleaned from citations and reference data can be used to cluster similar researchers. Web services, such as social bookmarking systems, provide new functionalities and a greater variety of social information - if exploited correctly; these could lead to better recommendations. The following describes, by way of example, one approach to author recommendation for social networking in academia.
引用
收藏
页码:261 / 272
页数:12
相关论文
共 50 条
  • [21] Using contextual information and multidimensional approach for recommendation
    Weng, Sung-Shun
    Lin, Binshan
    Chen, Wen-Tien
    EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (02) : 1268 - 1279
  • [22] Information Compensation Graph Contrastive Learning for Recommendation
    Zhenhai Wang
    Yunlong Guo
    Xiaoli Zhao
    Qi Liu
    Weimin Li
    Chang Liu
    Neural Processing Letters, 57 (3)
  • [23] Tourism Recommendation Using Social Media Profiles
    Kavitha, S.
    Jobi, Vijay
    Rajeswari, Sridhar
    ARTIFICIAL INTELLIGENCE AND EVOLUTIONARY COMPUTATIONS IN ENGINEERING SYSTEMS, ICAIECES 2016, 2017, 517 : 243 - 253
  • [24] Evolutionary Social Poisson Factorizationfor Temporal Recommendation
    ChunYan Yin
    YongHeng Chen
    Wanli Zuo
    International Journal of Computational Intelligence Systems, 14
  • [25] Social network recommendation system based on PPIN
    Zhang L.
    Zhang J.
    Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition), 2017, 47 (03): : 478 - 482
  • [26] Unknown but interesting recommendation using social penetration
    Jen-Wei Huang
    Hao-Shang Ma
    Chih-Chin Chung
    Zhi-Jia Jian
    Soft Computing, 2019, 23 : 7249 - 7262
  • [27] A Trusted Recommendation Method based on Social Circles
    Su, Chengjian
    Chen, Genlang
    Song, Guanghui
    PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ELECTRONIC TECHNOLOGY, 2016, 48 : 67 - 72
  • [28] SWAT: Social Web Application for Team Recommendation
    Braghin, Stefano
    Yong, Jackson Tan Teck
    Ventresque, Anthony
    Datta, Anwitaman
    PROCEEDINGS OF THE 2012 IEEE 18TH INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED SYSTEMS (ICPADS 2012), 2012, : 845 - 850
  • [29] Evolutionary Social Poisson Factorizationfor Temporal Recommendation
    Yin, ChunYan
    Chen, YongHeng
    Zuo, Wanli
    INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, 2021, 14 (01)
  • [30] An Enhanced Influence Diffusion Model for Social Recommendation
    Liu H.
    Zhang X.
    Yang B.
    Yun W.
    Zhao J.-Z.
    Jisuanji Xuebao/Chinese Journal of Computers, 2023, 46 (03): : 626 - 642